






'''simple docstring'''







from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__lowerCAmelCase        =							logging.get_logger(__name__)

__lowerCAmelCase        =							{
    "xlm-mlm-en-2048": "https://huggingface.co/xlm-mlm-en-2048/resolve/main/config.json",
    "xlm-mlm-ende-1024": "https://huggingface.co/xlm-mlm-ende-1024/resolve/main/config.json",
    "xlm-mlm-enfr-1024": "https://huggingface.co/xlm-mlm-enfr-1024/resolve/main/config.json",
    "xlm-mlm-enro-1024": "https://huggingface.co/xlm-mlm-enro-1024/resolve/main/config.json",
    "xlm-mlm-tlm-xnli15-1024": "https://huggingface.co/xlm-mlm-tlm-xnli15-1024/resolve/main/config.json",
    "xlm-mlm-xnli15-1024": "https://huggingface.co/xlm-mlm-xnli15-1024/resolve/main/config.json",
    "xlm-clm-enfr-1024": "https://huggingface.co/xlm-clm-enfr-1024/resolve/main/config.json",
    "xlm-clm-ende-1024": "https://huggingface.co/xlm-clm-ende-1024/resolve/main/config.json",
    "xlm-mlm-17-1280": "https://huggingface.co/xlm-mlm-17-1280/resolve/main/config.json",
    "xlm-mlm-100-1280": "https://huggingface.co/xlm-mlm-100-1280/resolve/main/config.json",
}




class   __SCREAMING_SNAKE_CASE       (__A       ):


  """simple docstring"""


  _a  : Dict      			=     '''xlm'''
  _a  : Dict      			=     {
      '''hidden_size''': '''emb_dim''',
      '''num_attention_heads''': '''n_heads''',
      '''num_hidden_layers''': '''n_layers''',
      '''n_words''': '''vocab_size''',  # For backward compatibility
  }







  def __init__(			self   ,	UpperCamelCase__=30_145   ,	UpperCamelCase__=2_048   ,	UpperCamelCase__=12   ,	UpperCamelCase__=16   ,	UpperCamelCase__=0.1   ,	UpperCamelCase__=0.1   ,	UpperCamelCase__=True   ,	UpperCamelCase__=False   ,	UpperCamelCase__=False   ,	UpperCamelCase__=False   ,	UpperCamelCase__=1   ,	UpperCamelCase__=True   ,	UpperCamelCase__=512   ,	UpperCamelCase__=2_048**-0.5   ,	UpperCamelCase__=1e-12   ,	UpperCamelCase__=0.02   ,	UpperCamelCase__=0   ,	UpperCamelCase__=1   ,	UpperCamelCase__=2   ,	UpperCamelCase__=3   ,	UpperCamelCase__=5   ,	UpperCamelCase__=True   ,	UpperCamelCase__="first"   ,	UpperCamelCase__=True   ,	UpperCamelCase__=None   ,	UpperCamelCase__=True   ,	UpperCamelCase__=0.1   ,	UpperCamelCase__=5   ,	UpperCamelCase__=5   ,	UpperCamelCase__=0   ,	UpperCamelCase__=0   ,	UpperCamelCase__=2   ,	UpperCamelCase__=0   ,	**UpperCamelCase__   ,	):






         """simple docstring"""


         a_						     =			vocab_size
         a_						     =			emb_dim
         a_						     =			n_layers
         a_						     =			n_heads
         a_						     =			dropout
         a_						     =			attention_dropout
         a_						     =			gelu_activation
         a_						     =			sinusoidal_embeddings
         a_						     =			causal
         a_						     =			asm
         a_						     =			n_langs
         a_						     =			use_lang_emb
         a_						     =			layer_norm_eps
         a_						     =			bos_index
         a_						     =			eos_index
         a_						     =			pad_index
         a_						     =			unk_index
         a_						     =			mask_index
         a_						     =			is_encoder
         a_						     =			max_position_embeddings
         a_						     =			embed_init_std
         a_						     =			init_std
         a_						     =			summary_type
         a_						     =			summary_use_proj
         a_						     =			summary_activation
         a_						     =			summary_proj_to_labels
         a_						     =			summary_first_dropout
         a_						     =			start_n_top
         a_						     =			end_n_top
         a_						     =			mask_token_id
         a_						     =			lang_id

         if "n_words" in kwargs:
                a_						     =			kwargs['n_words']

         super().__init__(pad_token_id=UpperCamelCase__   ,	bos_token_id=UpperCamelCase__   ,	**UpperCamelCase__	)





class   __SCREAMING_SNAKE_CASE       (__A       ):


  """simple docstring"""


  @property
  def    _a       (			self	):






         """simple docstring"""


         if self.task == "multiple-choice":
                a_						     =			{0: 'batch', 1: 'choice', 2: 'sequence'}
         else:
                a_						     =			{0: 'batch', 1: 'sequence'}
         return OrderedDict(
             [
                 ('input_ids', dynamic_axis),
                 ('attention_mask', dynamic_axis),
                 ('token_type_ids', dynamic_axis),
             ]	)












'''simple docstring'''







import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()




class   __SCREAMING_SNAKE_CASE       (__A							,       __A							,       unittest.TestCase       ):


  """simple docstring"""


  _a  : str      			=     CycleDiffusionPipeline
  _a  : int      			=     TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
      '''negative_prompt''',
      '''height''',
      '''width''',
      '''negative_prompt_embeds''',
  }
  _a  : str      			=     PipelineTesterMixin.required_optional_params - {'''latents'''}
  _a  : str      			=     TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({'''source_prompt'''}       )
  _a  : Optional[Any]      			=     IMAGE_TO_IMAGE_IMAGE_PARAMS
  _a  : Optional[int]      			=     IMAGE_TO_IMAGE_IMAGE_PARAMS







  def    _a       (			self	):






         """simple docstring"""


         torch.manual_seed(0	)
         a_						     =			UNetaDConditionModel(
             block_out_channels=(32, 64)   ,	layers_per_block=2   ,	sample_size=32   ,	in_channels=4   ,	out_channels=4   ,	down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')   ,	up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')   ,	cross_attention_dim=32   ,	)
         a_						     =			DDIMScheduler(
             beta_start=0.00_085   ,	beta_end=0.012   ,	beta_schedule='scaled_linear'   ,	num_train_timesteps=1_000   ,	clip_sample=UpperCamelCase__   ,	set_alpha_to_one=UpperCamelCase__   ,	)
         torch.manual_seed(0	)
         a_						     =			AutoencoderKL(
             block_out_channels=[32, 64]   ,	in_channels=3   ,	out_channels=3   ,	down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']   ,	up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']   ,	latent_channels=4   ,	)
         torch.manual_seed(0	)
         a_						     =			CLIPTextConfig(
             bos_token_id=0   ,	eos_token_id=2   ,	hidden_size=32   ,	intermediate_size=37   ,	layer_norm_eps=1e-05   ,	num_attention_heads=4   ,	num_hidden_layers=5   ,	pad_token_id=1   ,	vocab_size=1_000   ,	)
         a_						     =			CLIPTextModel(UpperCamelCase__	)
         a_						     =			CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'	)

         a_						     =			{
             'unet': unet,
             'scheduler': scheduler,
             'vae': vae,
             'text_encoder': text_encoder,
             'tokenizer': tokenizer,
             'safety_checker': None,
             'feature_extractor': None,
         }
         return components







  def    _a       (			self   ,	UpperCamelCase__   ,	UpperCamelCase__=0	):






         """simple docstring"""


         a_						     =			floats_tensor((1, 3, 32, 32)   ,	rng=random.Random(UpperCamelCase__	)	).to(UpperCamelCase__	)
         a_						     =			image / 2 + 0.5
         if str(UpperCamelCase__	).startswith('mps'	):
                a_						     =			torch.manual_seed(UpperCamelCase__	)
         else:
                a_						     =			torch.Generator(device=UpperCamelCase__	).manual_seed(UpperCamelCase__	)
         a_						     =			{
             'prompt': 'An astronaut riding an elephant',
             'source_prompt': 'An astronaut riding a horse',
             'image': image,
             'generator': generator,
             'num_inference_steps': 2,
             'eta': 0.1,
             'strength': 0.8,
             'guidance_scale': 3,
             'source_guidance_scale': 1,
             'output_type': 'numpy',
         }
         return inputs







  def    _a       (			self	):






         """simple docstring"""


         a_						     =			'cpu'  # ensure determinism for the device-dependent torch.Generator

         a_						     =			self.get_dummy_components()
         a_						     =			CycleDiffusionPipeline(**UpperCamelCase__	)
         a_						     =			pipe.to(UpperCamelCase__	)
         pipe.set_progress_bar_config(disable=UpperCamelCase__	)

         a_						     =			self.get_dummy_inputs(UpperCamelCase__	)
         a_						     =			pipe(**UpperCamelCase__	)
         a_						     =			output.images

         a_						     =			images[0, -3:, -3:, -1]

         assert images.shape == (1, 32, 32, 3)
         a_						     =			np.array([0.4_459, 0.4_943, 0.4_544, 0.6_643, 0.5_474, 0.4_327, 0.5_701, 0.5_959, 0.5_179]	)

         assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-2







  @unittest.skipIf(torch_device != 'cuda'   ,	'This test requires a GPU'	)
  def    _a       (			self	):






         """simple docstring"""


         a_						     =			self.get_dummy_components()
         for name, module in components.items():
                if hasattr(UpperCamelCase__   ,	'half'	):
                       a_						     =			module.half()
         a_						     =			CycleDiffusionPipeline(**UpperCamelCase__	)
         a_						     =			pipe.to(UpperCamelCase__	)
         pipe.set_progress_bar_config(disable=UpperCamelCase__	)

         a_						     =			self.get_dummy_inputs(UpperCamelCase__	)
         a_						     =			pipe(**UpperCamelCase__	)
         a_						     =			output.images

         a_						     =			images[0, -3:, -3:, -1]

         assert images.shape == (1, 32, 32, 3)
         a_						     =			np.array([0.3_506, 0.4_543, 0.446, 0.4_575, 0.5_195, 0.4_155, 0.5_273, 0.518, 0.4_116]	)

         assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-2







  @skip_mps
  def    _a       (			self	):






         """simple docstring"""


         return super().test_save_load_local()







  @unittest.skip('non-deterministic pipeline'	)
  def    _a       (			self	):






         """simple docstring"""


         return super().test_inference_batch_single_identical()







  @skip_mps
  def    _a       (			self	):






         """simple docstring"""


         return super().test_dict_tuple_outputs_equivalent()







  @skip_mps
  def    _a       (			self	):






         """simple docstring"""


         return super().test_save_load_optional_components()







  @skip_mps
  def    _a       (			self	):






         """simple docstring"""


         return super().test_attention_slicing_forward_pass()





@slow
@require_torch_gpu
class   __SCREAMING_SNAKE_CASE       (unittest.TestCase       ):


  """simple docstring"""


  def    _a       (			self	):






         """simple docstring"""


         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()







  def    _a       (			self	):






         """simple docstring"""


         a_						     =			load_image(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
             '/cycle-diffusion/black_colored_car.png'	)
         a_						     =			load_numpy(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy'	)
         a_						     =			init_image.resize((512, 512)	)

         a_						     =			'CompVis/stable-diffusion-v1-4'
         a_						     =			DDIMScheduler.from_pretrained(UpperCamelCase__   ,	subfolder='scheduler'	)
         a_						     =			CycleDiffusionPipeline.from_pretrained(
             UpperCamelCase__   ,	scheduler=UpperCamelCase__   ,	safety_checker=UpperCamelCase__   ,	torch_dtype=torch.floataa   ,	revision='fp16'	)

         pipe.to(UpperCamelCase__	)
         pipe.set_progress_bar_config(disable=UpperCamelCase__	)
         pipe.enable_attention_slicing()

         a_						     =			'A black colored car'
         a_						     =			'A blue colored car'

         a_						     =			torch.manual_seed(0	)
         a_						     =			pipe(
             prompt=UpperCamelCase__   ,	source_prompt=UpperCamelCase__   ,	image=UpperCamelCase__   ,	num_inference_steps=100   ,	eta=0.1   ,	strength=0.85   ,	guidance_scale=3   ,	source_guidance_scale=1   ,	generator=UpperCamelCase__   ,	output_type='np'   ,	)
         a_						     =			output.images

         # the values aren't exactly equal, but the images look the same visually
         assert np.abs(image - expected_image	).max() < 5e-1







  def    _a       (			self	):






         """simple docstring"""


         a_						     =			load_image(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
             '/cycle-diffusion/black_colored_car.png'	)
         a_						     =			load_numpy(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy'	)
         a_						     =			init_image.resize((512, 512)	)

         a_						     =			'CompVis/stable-diffusion-v1-4'
         a_						     =			DDIMScheduler.from_pretrained(UpperCamelCase__   ,	subfolder='scheduler'	)
         a_						     =			CycleDiffusionPipeline.from_pretrained(UpperCamelCase__   ,	scheduler=UpperCamelCase__   ,	safety_checker=UpperCamelCase__	)

         pipe.to(UpperCamelCase__	)
         pipe.set_progress_bar_config(disable=UpperCamelCase__	)
         pipe.enable_attention_slicing()

         a_						     =			'A black colored car'
         a_						     =			'A blue colored car'

         a_						     =			torch.manual_seed(0	)
         a_						     =			pipe(
             prompt=UpperCamelCase__   ,	source_prompt=UpperCamelCase__   ,	image=UpperCamelCase__   ,	num_inference_steps=100   ,	eta=0.1   ,	strength=0.85   ,	guidance_scale=3   ,	source_guidance_scale=1   ,	generator=UpperCamelCase__   ,	output_type='np'   ,	)
         a_						     =			output.images

         assert np.abs(image - expected_image	).max() < 2e-2




